We finetune our model on a subset of YFCC100M [1]} introduced for the MediaEval Placing Task MP-16 [2]}.
This subset includes \(4,219,225\)  geo-tagged images from FlickrAvailable at: https://multimedia-commons.s3-website-us-west-2.amazonaws.com.
The dataset contains ambiguous photos of indoor environments, food, and humans for which the location is difficult to predict.
During finetuning, we validate the performance on a validation set composed of \(22,855\)  geo-tagged images.
We finetune for 15 epochs using SGD wth a momentum of \(0.9\) , a weight decay of \(1e-4\) , and a learning rate of \(0.05\) .
We decay the learning rate by \(0.01\)  at epochs \([8, 12]\) .
Finally, we evaluate the finetuned model on the im2gps [3]} test set, containing 237 geo-tagged images.
We perform inference and record the top predicted cell for each image in the test set.
The predicted cells are mapped back to a geographical latitude and longitude and the great circle distance (GCD) is computed by comparing to the ground truth latitude/longitude.
